Utility for Creating Heatmaps for the Study of Competitive Advantage in the Restaurant Marketplace

ABSTRACT

A utility builds and displays heatmaps of competitive activity that aid in studying the competitive advantage of a particular restaurant. The heatmaps are constructed based on activity logged with a restaurant service, and available within the restaurant service&#39;s database(s).

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. §120 as acontinuation-in-part of co-pending U.S. patent application Ser. No.13/337,347, titled “UTILITY FOR DETERMINING COMPETITIVE RESTAURANTS,”filed on Dec. 27, 2011, which is hereby incorporated by reference in itsentirety.

FIELD OF THE INVENTION

The present invention relates to a system and method of studying thecompetitive advantage of a retail business and its competitors, and moreparticularly relates to a system and method for creating maps of retailactivity for a retail business and its competitors, and moreparticularly still, for creating maps of pickup and delivery dineractivity for a restaurant and its competitors using a database assembledby a restaurant service of which the restaurant and its competitors aremembers.

DESCRIPTION OF THE PRIOR ART

businesses naturally desire to know who their most important competitorsare. Generally, any two businesses that market to the same customers arein competition. For example, a suburban convenience store is, in thebroadest sense, in competition with a nearby grocery store, as dollarsspent by customers in the grocery store cannot be spent in theconvenience store. However, experience shows that a gas station locateda mile away and offering basic convenience items is a far more importantcompetitor to the convenience store than the grocery store across thestreet, as customers will look to the gas station for the same types ofpurchases that they look to the convenience store; i.e., impulsepurchases or purchases that must be executed quickly.

Within the restaurant field, the situation becomes complex very quickly.There are numerous cuisine types, such as Asian and French, and withineach cuisine type, there are often multiple levels of dining experience.For example, within Asian restaurants, there are fast-food Asianrestaurants, as well as fine-dining Asian restaurants, and multiplelevels in between. Cuisine types can be subdivided even further; forexample, there are Chinese, Japanese, Thai, and Vietnamese restaurants,all of which are “Asian Restaurants,” and all of which offer verydifferent types of food from one another, and are likely to attractdiners with very different tastes.

To further complicate matters, within the restaurant field, restaurantsoffering very different types of food can be considered competitorsunder certain circumstances. For example, an urban Mexican restaurantprimarily serving the lunch crowd is likely in direct competition withnearby sandwich restaurants, pizza restaurants, and Asian restaurantsserving the same crowd.

One way that restaurants determine their closest competitors is toconsult market surveys. For example, a restaurant may setup an onlinepage that, in exchange for access to coupons or other “bonuses,” gathersinformation about customers' dining habits by asking diners to answersurveys. Another way that this can be accomplished would be by placing atracking cookie on a user's computer and checking the cookie each time acustomer visits the restaurant's web page for the identity of otherrestaurant's web pages. Other, similar techniques have been employed inthe past.

There are a number of problems with such “market survey” approaches. Oneproblem is that the significance of market surveys is difficult toquantify. For example, the relevance of a restaurant's customer visitingthe website of a competitor is questionable. Another issue with suchsurveys is that they are expensive and time consuming to conduct.

Another problem facing businesses is that once a particular competitoris identified, it is difficult to understand in what areas thecompetitor excels, and in what areas the competitor is vulnerable.Similarly, many businesses do not understand their own strengths andweaknesses when compared to their most important competitors. Theseinquiries are critical to the development of competitive advantage.Returning to the restaurant field, a particular competitor may excel atserving light meals to the lunch crowd, while not having an appropriateselection of more filling meals for lunch patrons. The same restaurantmay have entirely different competitive strengths with regards to dinnerand breakfast patrons, or it may not serve them at all. Based on thesekinds of assessments, a restaurant that excels at providing hearty mealsto the lunch crowd could appropriately position itself against itslight-lunch serving competitor.

Most restaurants do not make any formal study of other restaurantscompetitive advantage. For those that do, the usual way for a restaurantto obtain a better understanding of the competitive advantage ofcompeting restaurants is through a study of reviews conducted bycritics, surveys conducted with the restaurant's own customers, andactual investigation of the restaurant's offerings through visits to thecompeting restaurant. This method of market research, while wellunderstood, leaves much to be desired. In particular, it is extremelytime consuming, and the actual determination of a competitor's strengthsand weaknesses is highly subjective.

Certain businesses have made use of maps that play back purchases byconsumers at their establishment. For example, online retailerZappos.com makes a map of recent purchases available. This map displayssome or all online orders that are placed, along with an indication ofwhere the purchase was placed from. For example, if a customer inRaleigh, N.C. purchases a set of shoes, a brief descript of the shoesalong with an abbreviated version of the purchasers name, such as M. S.(for Mary Smith), is displayed in a small “bubble” for a brief period oftime, with the tail of the bubble tracing back to the purchasersapproximate location. In addition, restaurant service provider GrubHub,Inc. operates a map that resides in the lobby of their Chicagoheadquarters as well as the lobby of their New York office that displaysorders in near real-time in certain markets. These displays providestrong visual clues as to where ordering activity is taking place at anygiven time.

Objects of the Disclosed Competitive Advantage Utility

An object of the disclosed competitive advantage utility is to providean automated means of studying the competitive advantage of specificallyidentified competitors for a business;

Another object of the disclosed competitive advantage utility is toprovide an automated means of studying the competitive advantage of allcompetitors, or an identified subset of competitors, within a particulardistance of a subject business;

Another object of the disclosed competitive advantage utility is tographically depict activity of all competitors or an identified subsetof competitors;

Another object of the disclosed competitive advantage utility is tographically depict the activity of competitive restaurants within aparticular distance of a subject business;

Another object of the disclosed competitive advantage utility is tographically depict the activity of competitive restaurants within aspecified time period;

Other advantages of the disclosed shopping service will be clear to aperson of ordinary skill in the art. It should be understood, however,that a system, method, or apparatus could practice the disclosedcompetitor utility while not achieving all of the enumerated advantages,and that the protected shopping service is defined by the claims.

SUMMARY OF THE INVENTION

A utility for studying the competitive advantage of a particularrestaurant displays a heatmap of competitive activity. In particular, adatabase maintains an order queue. A restaurant server with access tothe database communicates with a computer system over a network, suchas, for example, the Internet. The computer system transmits anidentifier to the restaurant server that uniquely identifies it. Thecomputer system also transmits an indicia indicative of desiredcompetitor characteristics to the restaurant server. The restaurantserver queries the database based on the indicia and the identifier todetermine a set of competitors for the subject restaurant. Therestaurant server then compiles a list of competitors and transmits themto the computer system, which specifies a map area that includes thesubject restaurant and transmits the map area to the restaurant server.The restaurant server queries the database for competitor activitywithin the map area and generates competitive activity events that aretransmitted to the computer system. The computer system renders thecompetitive activity events on a display.

BRIEF DESCRIPTION OF THE DRAWINGS

Although the characteristic features of this invention will beparticularly pointed out in the claims, the invention itself, and themanner in which it may be made and used, may be better understood byreferring to the following description taken in connection with theaccompanying drawings forming a part hereof, wherein like referencenumerals refer to like parts throughout the several views and in which:

FIG. 1 is a simple system diagram of a system implementing the disclosedcompetitor utility;

FIGS. 2 a and 2 b are a flowchart illustrating a first process by whicha utility can programmatically determine the competitors of a subjectrestaurant;

FIG. 3 is a flowchart illustrating a process by which a cuisine typematch score between a subject restaurant and a competitor restaurant canbe calculated;

FIG. 4 is a flowchart illustrating a process by which a distance scorecan be calculated between a subject restaurant and a competitorrestaurant can be calculated;

FIG. 5 is a flowchart illustrating a second process by which a utilitycan programmatically determine the competitors of a subject restaurant;

FIG. 6 is a flowchart illustrating a process by which the menu of asubject restaurant can be compared with the menus of competitorrestaurants;

FIG. 7 is a flowchart illustrating a process by which the order historyof a subject restaurant can be compared with the order histories ofcompetitor restaurants;

FIG. 8 is a flowchart illustrating a third process by which a utilitycan programmatically determine the competitors of a subject restaurant;

FIGS. 9 a and 9 b comprise a flowchart illustrating a fourth process bywhich a utility can programmatically determine the competitors of asubject restaurant;

FIG. 10 is a flowchart illustrating a process by which the hours ofoperation of a subject restaurant can be compared with the hours ofoperation of the subject restaurant's potential competitors;

FIG. 11 is a flowchart illustrating a process by which the diner ratingsof a subject restaurant can be compared with the diner ratings of thesubject restaurant's potential competitors;

FIG. 12 is a flowchart illustrating a process by which by which thedelivery radius of a subject restaurant can be compared with thedelivery radii of the subject restaurant's potential competitors;

FIG. 13 is a flowchart illustrating a process by which the delivery feesof a subject restaurant can be compared with the delivery fees of thesubject restaurant's potential competitors;

FIG. 14 is a flowchart illustrating a process by which the orderminimums of a subject restaurant can be compared with the order minimumsof the subject restaurant's potential competitors; and

FIG. 15 is a simplified system diagram of a system implementing thedisclosed competitive advantage utility;

FIG. 16 is a flowchart illustrating a process by which a heatmap ofcompetitive activity can be assembled and displayed;

FIG. 17 is a displaying a tool for selecting a map area for which todisplay a heat map for;

FIGS. 18 a-c are a set of heat maps for a small area about a subjectrestaurant;

FIGS. 19 a-c are a set of heat maps for a larger area about a subjectrestaurant;

FIG. 20 is a flowchart illustrating a process by which a heatmap ofaggregate competitive activity can be assembled and displayed;

FIGS. 21 a-d are a sequence of heatmaps illustrating aggregatedcompetitive activity for the displayed neighborhood at different times;

FIG. 22 is a flowchart illustrating a process by which relative rankingsof aggregated competitive activity generated in accordance with aplayback schedule can be ranked;

FIGS. 23 a-d are illustrations of various ways to display relativeamounts of aggregated competitive activity; and

FIG. 24 is a line graph illustration of order activity for the subjectrestaurant over a time period.

DETAILED DESCRIPTION OF THE ILLUSTRATED EMBODIMENT

I. Utility for Determining Competitors of a Business

Turning to the Figures, and to FIG. 1 in particular, a systemimplementing the disclosed competitor locator utility is depicted. Inparticular, the system comprises a computer 12, which could be, forexample, a personal computer or a smart phone, with access to a database14. The computer 12 executes software, referred to as the competitorutility. The database contains records of restaurants, including therestaurants' names, location, a general description of the type ofcuisine offered by each restaurant, a detailed menu comprising the itemsoffered for sale and their prices, and an order history for eachrestaurant. Such a database may be compiled by, for example, a marketresearch company covering the restaurant business, or a restaurantservice, providing delivery and/or order placement services for a largenumber of restaurants, or any other entity having an interest inrestaurants.

FIGS. 2 a and 2 b comprise a flow chart depicting a first process bywhich software with access to database 14 can assemble a list of themost important competitors to a subject restaurant. In a first step 20,a number of user inputs are accepted, including identifying the subjectrestaurant, specifying whether restaurants providing their servicethrough pickup, delivery, either pickup or delivery, or both pickup anddelivery are to be included in the search, and a maximum radius tosearch in. In step 22, the utility queries database 14 for allrestaurants within the market of the subject restaurant; e.g., for arestaurant in Chicago, Ill., all restaurants within the Chicago areawill be returned, as all of them are potentially competitors of thesubject restaurant.

Execution then transitions to step 24, where the first of two filtersare applied. The first filter removes all restaurants that do notprovide the chosen pickup or delivery service, and assembles competitorset 1. For example, if delivery were chosen above, all restaurants thatprovide only pickup service would be filtered out of competitor set 1.Execution then transitions to step 26, where all restaurants outside ofthe specified maximum radius are filtered out. Allowing a user tospecify a maximum radius allows the user to apply judgment regarding thespecific situation facing the subject restaurant. For example, adowntown restaurant may use a much smaller maximum radius than arestaurant within a residential neighborhood.

After assembling competitor set 2, the utility then generates a cuisinematch score for each restaurant within competitor set 2 in step 28. Thedetails of how a cuisine match score can be generated are discussedbelow with regard to FIG. 3. Execution then transitions to step 30,where a distance score is generated for reach restaurant withincompetitor set 2, as further discussed below with regards to FIG. 4.

The scores generated in steps 28 and 30 are then weighted and combinedto form a competitor score for each restaurant within competitor set 2.The scores can be combined with any desired weighting; for example, thecuisine score can be weighted by a factor of 0.8, while the distancescore can be weighted by a factor of 0.2. In step 34, the lowest scoringcompetitors are discarded, so that only the most relevant competitorsare displayed. There are a number of ways that competitors can betrimmed from the list; for example, the lowest scoring restaurant can beremoved, or a percentage, such as 30%, of the lowest scoring restaurantscan be removed. Finally, in step 36, the competitor restaurants arepresented by the utility.

FIG. 3 is a flow chart that illustrates the process by which a cuisinetype match score can be computed. In step 50, the database record forthe subject restaurant is retrieved from the database. In step 52, acheck is made to determine if there are more competitor restaurants incompetitor set 2 to compute cuisine type match scores for. If there arenot, the process exits in step 54. However, if there are more competitorrestaurants in competitor set 2, execution transitions to step 56, wherethe next competitor restaurant in competitor set 2 is retrieved. In step58, the cuisine type match score for the present competitor restaurantis computed, and in step 60, the cuisine type match score for thepresent competitor is saved for future use. Execution then returns tostep 52.

One way that the cuisine type match score could be computed would be tocompute the number of matching cuisine types between the presentcompetitor restaurant and the subject restaurant, and then dividing thenumber of matches by the total number of cuisine types of the subjectrestaurant. Cuisine type is a broad indication of the type of food thata restaurant serves, and many restaurants will cover multiple cuisinetypes. A non-exhaustive list of example cuisine types are: African,Argentinian, barbecue, bagels, bakery, Brazilian, Cajun, Cantonese,Caribbean, Chicken, classic, Colombian, Cuban, deli, dessert, Dim Sum,eclectic, Ecuadorian, fine dining, French, fresh fruits, German, Greek,grill, hoagies, ice cream, Indian, Irish, Jamaican, Japanese, kids menu,Korean, Kosher, late night, Latin American, Lebanese, low carb, low fat,Malaysian, Mandarin, Mediterranean, Mexican, Middle Eastern, noodles,organic, Persian, Peruvian, Polish, Portuguese, Puerto Rican, ribs,Russian, seafood, soul food, soup, South American, Spanish, steak, subs,sushi, Szechwan, tapas, Thai, Turkish, vegan, vegetarian, Vietnamese,wings, and wraps.

Accordingly, if the subject restaurant has cuisine types of Brazilian,fine dining, grill, late night, low carb, and South American, and acompetitor restaurant has cuisine types of classic, Cuban, fine dining,and Latin American, there would be matches on only 1 cuisine type out of6. If another competitor restaurant has cuisine types of Brazilian, finedining, South American, and wraps, there would by matches on 3 cuisinetypes out of 6. Accordingly, the first competitor restaurant would havea cuisine type match score of 1/6, while the second restaurant wouldhave a cuisine type match score of 1/2 (3/6).

FIG. 4 is a flowchart that illustrates the process by which a distancescore can be computed for competitor restaurants. In step 70, the recordof the subject restaurant is retrieved. Execution then transitions tostep 72, where a check is made to determine if there are more competitorrestaurants. If not, execution transitions to step 74 where the processis exited. If there are additional competitor restaurants, executiontransitions to step 76, where the next competitor restaurant record isretrieved. In step 78, a distance score for the present competitorrestaurant is computed, and in step 80, the distance score for thepresent competitor restaurant is stored for later use.

One way that a distance score for a competitor restaurant can becomputed is through the use of the Gaussian function, which assumes thatthe preference of consumers regarding the distance to a restaurant whichthey are willing to patronize is normally distributed. The Gaussianformula can be expressed as:

${f(x)} = {a\; ^{- \frac{{({x - b})}^{2}}{2\; c^{2}}}}$

Where e is Euler's number (approximately 2.718281828), a is a constantthat sets the high of the distributions peak, b is the center value ofthe peak, and c controls the width of the resultant distribution.Accordingly, for a distribution centered at 0 (meaning that mostconsumers will prefer restaurants closer to them) with a maximum peak of1.0, and an approximate width of 2.0 miles, the following equationresults.

${f(x)}^{- \frac{{(x)}^{2}}{2 \times 2^{2}}}$

Accordingly, for a competitor restaurant that is 2 miles away from thesubject restaurant would have a score of approximately 0.60653.

In FIG. 5, a flow chart depicting a second process by which softwarewith access to database 14 can assemble a list of most importantcompetitors for a subject restaurant. In a first step 102, a recordcorresponding to the subject restaurant is retrieved from the database14. In the next step 104, a database query is made to determine thoserestaurants within a maximum pickup distance of the subject restaurant.Then, those restaurants that have a different basic cuisine type arestripped out in step 106; i.e., if the subject restaurant primarilyserves pizza, then Asian restaurants, French Restaurants, Fine DiningRestaurants, etc., will be stripped out, as they are not truly“competitors” of the subject restaurant.

In step 108, the menus of the remaining competitor restaurants arecompared with the menu of the subject restaurant. Similarly, in step110, the order histories of the remaining competitor restaurants arecompared with the order history of the subject restaurant. Based on thecomparisons in step 108 and step 110, a list of most importantcompetitors is assembled and output in step 112. Generally, a competitorvalue is assigned to each competitor in step 112, which is a combinationof the menu value derived in step 108 and the order history orderhistory value derived in step 110, both of which are addressed in moredetail hereafter. It should be noted that the different values can becombined in a weighted fashion; for example, the menu value may receivea weight of 2, while the order history value receives a weight of 1, andthe competitor score will be the addition of the two weighted scores.

It is mentioned above in step 108 that the menu of the subjectrestaurant is compared with the menus of the remaining competitorrestaurants. One way this can be accomplished is illustrated in the flowchart of FIG. 6. It should be noted that this process assumes that thesubject restaurant has already been retrieved into memory. Accordingly,the process begins at step 150, where a check is made to determine ifall of the competitor restaurants have been compared to the subjectrestaurant. If there are no competitor restaurants, executiontransitions to step 168, where the process is exited. However, if thereare more competitor restaurants to compare against the subjectrestaurant, execution transitions to step 152, where the next competitoris retrieved and becomes the present competitor restaurant. Executionthen transitions to step 154, where a check is made to determine if allof the subject restaurant's menu items have been scored against thepresent competitor restaurant. If the subject restaurant has more menuitems to score against the present competitor restaurant, executiontransitions to step 156, where the next menu item of the subjectrestaurant is retrieved.

Execution then transitions to step 158 where a check is made todetermine if the present competitor restaurant has more menu items tocompare with the present menu item of the subject restaurant. If so,execution transitions to step 160, where the next menu item of thepresent competitor restaurant is retrieved. In step 162, the presentmenu item of the subject restaurant and the present competitorrestaurant are compared and scored, and in step 164 the menu match scoreof the competitor restaurant is incremented by the match score of thelast menu item.

Returning to step 154, if the subject restaurant does not have more menuitems, execution transitions to step 166, where the score for thepresent competitor restaurant is stored. Execution then returns to step150, where execution proceeds as previously discussed. With regards tostep 158, if the present competitor restaurant does not have any moremenu items, execution returns to step 154, where execution proceeds aspreviously discussed.

Step 162 discussed above requires that the menu items of two restaurantsare compared and scored based on how similar they are. There are avariety of ways that this can be done. The simplest way to do this wouldbe to compare the names of the menu items using a simple stringcomparison. Items that exactly matched could, for example, be given avalue of one (1), while items that did not match could be given a valueof zero (0). However, this method would not provide a very reliablecomparison, as restaurants are likely to give different names to similaritems. For example, one restaurant may call a hamburger with cheese a“cheeseburger,” while another restaurant may refer to it as an “oldfashioned burger with cheese.”

An improved method of determining how close two menu items are to oneanother would be to employ a preprocessor, which would classify menuitems for each restaurant using a common nomenclature. The preprocessor,which could be employed at the time that menus were input into thedatabase or at any time thereafter, could produce an extra fieldassociated with each menu item. Within the extra field, hereinafterreferred to as the true item field, a hamburger with cheese would be thesame regardless of the title that the restaurant gave it.

The comparison of true items would also allow for more granular gradingof matches. For example, each true item could store a similarity toevery other true item. Accordingly, a hamburger could be given asimilarity of 0.9 to a cheeseburger, and vice-verse. However, such anapproach would necessarily require a large amount of information to beassembled and stored for every food item. In particular, for a databaseof N true items, each true item would need to store an additional N−1fields to account for its match to every other true item.

A different approach would be to group true items into a variety oftypes, such as salads, soups, lunch sandwiches, pizzas, steaks, etc.Simple items, such as a standard salad, would be assigned a 1.0 for thetype salads, and a 0.0 for the remaining types. More difficult toclassify items, such as a steak salad, would be assigned non-zero valuesfor multiple types, such as 0.75 for salads, 0.25 for steaks, and 0.0for the remaining fields. When two of the same true items were compared,a match score of 1.0 could be assigned, but when comparing differenttrue items the scores in each type could be multiplied together andadded to form a match score. For example, when comparing a steak saladto a regular salad, a match score of 0.75 would result.

Another way of determining how close two menu items are to one anotherwould be to compare constituent ingredients. This would, of course,require that the database actually store the ingredients that comprisethe various menu items. If the ingredients are available, eachingredient of the two menu items could be compared, and a score derivedfrom the comparison. For example, if a menu item of a subject restauranthas ten ingredients, and a menu item of a competitor restaurant matchessix ingredients, a match score of 0.6 could result. This model can, ofcourse, be further adjusted so that only similar cuisines are compared.For example, a steak salad could give a high match score if compared toa steak taco, despite being very different food.

Other factors that can be used to compare menu items include dietaryvalue, such as, for example, low-calorie, low carbohydrate, vegetarian,vegan, and gluten free, as well as food quality, such as standard,all-natural, organic, and locally produced or grown.

Returning to FIG. 5, step 110 requires a comparison of the order historyof the subject restaurant with competitor restaurants, to reflect theprinciple that a restaurant that serves gourmet sandwiches and burgersis not likely to compete directly with a fast food restaurant servinglunch sandwiches and burgers, even though their menus may besubstantially identical. FIG. 7 is a flowchart illustrating a process bywhich order histories of restaurants can be compared and scored forsimilarity.

Starting with a step 202, an order history value for the subjectrestaurant is computed. Execution then transitions to step 204, where acheck is made to determine if there are more competitor restaurants withwhich to compare order histories. If none remain, execution transitionsto step 214 where the order history comparison process exits. However,if additional competitor restaurants remain, the next competitorrestaurant is retrieved and marked as the present competitor restaurantin step 206. The order history of the present competitor restaurant isretrieved and an order history value is computed in step 208, and acomparison between the order history values of the present competitorrestaurant and the subject restaurant is conducted and scored in step210. Execution then transitions to step 212, where the order historyscore for the competitor restaurant is saved. Execution then returns tostep 204, where execution proceeds as previously discussed.

The above process discussed computing the “order history value” for thesubject restaurant and each competitor of the subject restaurant. Oneway that an order history value can be computed would be to compute themean, or average, value of an order for the entirety of a restaurant'sorder history, or a subsection thereof. It is also fairly simple todetermine if a restaurant's average order size is similar to anotherrestaurant, and therefore, it is fairly simple to “score” the similarityof one restaurant's order history to another using the average ordersize. For example, if one restaurant has an average order size of $29,while another has an average order size of $11, it is likely that thetwo restaurants are not in direct competition; on the contrary, if onerestaurant has an average order size of $13 and another has an averageorder size of $11, it is far more likely that they are in competition.Given the above, one way to compare the average values would be to use afixed range value, such as $3.50, as an offset from the average ordersize. Accordingly, if a subject restaurant had an average order size of$10, any restaurant's with an average order size of between $6.50 and$13.50 would be judged a potential competitor, and given an orderhistory competition score of 1, while restaurant's with an order sizeoutside of that range would be given a score of 0. One simple variationwould be to vary the range value based on the type of restaurant. Forexample, fast food restaurants could use a range value of $1.80, whilepizza restaurants could use a range value of $3.20, etc.

Another simple extension of the above would be to vary the size of theoffset used for comparison based on the order history. One way to dothis would be to use a fixed percentage of the average order size, suchas, for example 10%. Another way would be to use the standard deviationof the order history subset from which the average value was computed.Standard deviation of a set can be computed using the following formula:

σ=√{square root over (Σ_(i=1) ^(n)(m−x _(i))²)}

Where σ is the standard deviation, m is the mean value of a set having nmembers, and x_(i) is the ith member of a set having n members.Expressed in words, the standard deviation is the square root of the sumof the square of the difference between the mean and each set member.For example, assume that a subject restaurant has an order history withthe following five order amounts: $13, $9, $17, $11, $11.50. The mean,or average of these orders would be $12.30, and the standard deviationwould be $5.98.

Assuming that order sizes are normally distributed, one standarddeviation from the mean will account for 68.27% of all orders within theset of orders used to compute the standard deviation, while using twostandard deviations would account for 95.45% of orders. Accordingly,given the example above, one way of grading the orders would be to usean “all-or-nothing” scoring system, and thereby assign a value of 1 toany restaurant whose average order size is within, for example, 1 or 2standard deviations of the order size of the subject restaurant.

A further refinement that can be applied to any of the order historycompetitive scoring systems discussed above would be to scale thecompetitive score of a restaurant based on how far apart the averageorder sizes Were. With reference to the last system discussed above, ifa subject restaurant has an average order size of $10 and a standarddeviation of $1, a zero value could be assigned to restaurants with, forexample, an average order size 3 standard deviations away; i.e., lessthin or equal to $7 or greater than or equal to $13. Intermediate valuescould be linearly interpolated within that range, so that competitiverestaurants with an average order size identical to that of the subjectrestaurant, $10 in this case, would be given a value of 1. Accordingly,a competitive restaurant with an average order size of $8.50 or $11.50would be assigned an order history score of 0.5, etc.

A simpler way of comparing the order history of the subject restaurantwith a competitor's order history would be to compare the daily volumeof orders that each restaurant processed. This could be done bycomparing average daily order volume as discussed earlier for ordersize, with the average daily order volume being computed, for example,over a week, a month, or a quarter. Similarly, standard deviation oforder volume computed over a period can also be useful for comparing theorder volume of a subject restaurant and a competitor restaurant.

An additional, longer term, comparison of order histories can also beused to determine if the subject restaurant and a particular competitorhave a similar seasonal pattern. If the seasonal pattern divergessignificantly, this can be indicative that the restaurants may onlycompete part of the year, rather than year round. One way to compute aseasonal pattern for the sales of a restaurant would be to compute thetotal sales for a quarter, and compare those sales on aquarter-to-quarter basis with other quarters. Similarly, sales could becomputed on a month of the year basis, or a week of the year basis. Thena comparison can be made from one time period to another; i.e.,quarter-to-quarter, month-to-month, week-to-week, etc., to determine ifparticular time periods consistently perform better or worse than othertime periods. For example, it would be expected that ice cream parlorsin the Midwest would perform consistently better in summer than winter,while other restaurant's would likely have the opposite pattern. Bycomparing seasonal patterns of potentially competing restaurant'scompetitors that are not readily apparent may be found. For example,competitors of restaurants that specialize in serving Holiday partiesmay not be easily determined in other ways.

One way to compare the seasonal pattern of sales for a subjectrestaurant, and a potential competitor restaurant would be to calculatethe total sales for each on a monthly basis over the period of, forexample, five years. An average monthly sales number can then becomputed, and the standard deviation calculated as discussed previously.Each month can then be compared to the average and the standarddeviation, and, months that vary from the standard deviation by morethan some particular value can be computed. The month-to-month variationof the subject restaurant can then be correlated with the month-to-monthvariation of the potential competitor restaurant using, for example, aPearson correlation analysis. The correlation analysis will return avalue between 0 and 1, which can be used as a seasonal pattern scorethat is indicative of the level of competition between the tworestaurants.

One issue with computing a seasonal pattern is determining if a trend ispresent in the seasonal data, such as would be present for a rapidlygrowing (or shrinking) restaurant. However, there are numerous methodswell-known in the art to detrend data. For example, the use of detrendedfluctuation analysis can be used to remove the trend component of theanalyzed order data.

Another improvement that can be made is to more accurately model theradius within which diners search for pickup and delivery restaurants.One way that this can be done is to acknowledge that diners will havevariable tolerances for the distance that they will look forrestaurants. While one diner may be willing to patronize onlyrestaurants within 8 blocks of his residence, another diner may bewilling to patronize restaurants as far away as 5 miles from herresidence. The preferences of diners as to a maximum radius they arewilling to travel to patronize a pickup or delivery restaurant is likelyto vary with a number of factors. For example, diners in New York, whousually walk, may uniformly have a lower maximum radius than diners inLos Angeles, who usually drive. However, some variance among the dinersin each market is likely. Accordingly, one way to model dinerpreferences for their maximum restaurant patronizing distance would beconduct market surveys to determine a distribution function for eachmarket area, such as a particular city, or a particular neighborhoodwithin a city. Alternatively, a restaurant service could actually accessdata regarding diners ordering habits and determine for a particularmarket the distribution of distances that diners place orders withrestaurants. Assuming a more or less normal distribution of preferencesamong diners, a distribution function can be modeled with a mean maximumrestaurant patronizing distance, and a standard deviation.

To make effective use of a distribution function for the maximumrestaurant patronizing distance, a modification to the process of FIG. 5must be made, as is reflected in FIG. 8. In step 302, the record for thesubject restaurant is retrieved from the database. In step 304, amaximum radius is determined. One way of doing this would be to use adistance 3 standard deviations greater than the mean maximum restaurantpatronizing distance, which should account for approximately 99.7% ofdiners' preferences.

In step 306, restaurants within the “maximum radius” are retrieved fromthe database, and are assigned a distance value. One way that this canbe done would be to linearly interpolate based on distance, so thosecompetitor restaurants that are very close to the subject restaurant areassigned a distance score close to 1, while those that are far away,e.g., close to a 3 standard deviations of the maximum restaurantpatronizing distance distribution function away, would be assigned adistance value close to 0.

In step 308, the subject restaurant's menu is compared with thecompetitor restaurant menus, and each competitor restaurant is assigneda menu score. The comparison process can, for example, follow theprocess outlined in FIG. 3. Execution then transitions to step 310,where the order history of the subject restaurant and the competitorrestaurants are compared, using, for example, the process outlined inFIG. 4. In step 312, the distance value, the menu value and the orderhistory values are combined, and a set of competitors, each with acompetition score, is assembled and output.

FIGS. 9 a and 9 b comprise a flow chart depicting a third process bywhich software with access to database 14 can assemble a list of mostimportant competitors for a subject restaurant. In a first step 402, arecord corresponding to the subject restaurant is retrieved from thedatabase 14. In the next step 404, a maximum radius to search forcompetitor restaurants is determined, and, in step 406, a database queryis made to determine those restaurants within the maximum search radius.In step 408, the menus of the remaining competitor restaurants arecompared with the menu of the subject restaurant. Similarly, in step410, the order histories of the remaining competitor restaurants arecompared with the order history of the subject restaurant. Unlike theprocess of FIG. 8, however, this process adds a number of additionalsteps to further improve competitor comparisons.

In step 412, the hours of operation of the subject restaurant arecompared with the hours of operation of the competitor restaurants andan hours of operation score for each potential competitor is developed.In step 414, diner ratings for the subject restaurant are compared withthe diner ratings of the competitor restaurants, and a diner ratingscore for each potential competitor is developed. In step 416, thedelivery radius of the subject restaurant is compared with the deliveryradius of the competitor restaurants, and a delivery radius score foreach potential competitor is developed. In step 418, delivery feescharged by the subject restaurant are compared with delivery feescharged by competitor restaurants, and a delivery fee score for eachpotential competitor is developed. In step 420, order minimums for thesubject restaurant are compared with order minimums for the competitorrestaurants, and an order minimum score for each potential competitor isdeveloped.

FIG. 10 is a flow chart that illustrates the process by which the hoursof operation for a subject restaurant can be compared with the hours ofoperation of its competitors, and an hours of operation score can beprogrammatically computed. In step 502, the database record for thesubject restaurant is retrieved from the database. In step 504, a checkis made to determine if there are more competitor restaurants to comparehours of operation with. If there are not, the process exits in step512. However, if there are more competitor restaurants, executiontransitions to step 506, where the next competitor restaurant isretrieved. In step 508, the hours of operation of the subject restaurantand the present competitor restaurant are compared and an hours ofoperation score is computed. In step 510, the hours of operation scorefor the present competitor is saved for future use. Execution thenreturns to step 504.

An hours of operation score that is indicative of the degree ofcompetition between a subject restaurant and a potential competitor canbe developed by comparing the degree of overlap of operating hoursbetween the subject restaurant and the potential competitor. Forexample, if the subject restaurant is open from 6:30 AM through 2:00 PM,a total of 7.5 hours, and a competitor is open from 11 AM through 9 PM,the competitor's hours of operation match a total of three hours of thesubject restaurant, which would give a score of 3/7.5, or 0.4.

FIG. 11 is a flow chart that illustrates the process by which dinerratings for a subject restaurant can be compared with its competitors'diner ratings, and a diner ratings score can be programmaticallycomputed. In step 602, the database record for the subject restaurant isretrieved from the database. In step 604, a check is made to determineif there are more competitor restaurants to diner ratings with. If thereare not, the process exits in step 612. However, if there are morecompetitor restaurants, execution transitions to step 606, where thenext competitor restaurant is retrieved. In step 608, the diner ratingsof the subject restaurant and the present competitor restaurant arecompared and a diner ratings score is computed. In step 610, the dinerratings score for the present competitor is saved for future use.Execution then returns to step 604.

Assuming that diners are required to assign a numeric value to aparticular restaurant, then a diner ratings score that is indicative ofthe degree of competition between a subject restaurant and a potentialcompetitor can be developed by computing the average value of thesubject restaurant's diner ratings, and comparing that with the averagevalue of a competitor's diner ratings. One way to compute a dinerratings score indicative of the degree of competition between therestaurants would be to use the following formula:

$S = \frac{S_{Max} - {{R_{AS} - R_{A\; C}}}}{S_{Max}}$

Where, S is the computed diner rating score, SMax is the maximumpossible diner rating score, R_(AS) is the average rating of the subjectrestaurant, and R_(AC) is the average rating of the competitorrestaurant.

For example, if the diner ratings of the subject restaurant have anaverage value of 4.0 (out of 5) and the diner ratings of a competitorhave an average value of 3.2 (out of 5), then, using the formula above,the diner rating score would be (5−0.8)/5=0.84.

FIG. 12 is a flow chart that illustrates the process by which thedelivery radius of a subject restaurant can be compared with itscompetitors' delivery radii, and a delivery radius score can beprogrammatically computed. In step 702, the database record for thesubject restaurant is retrieved from the database. In step 704, a checkis made to determine if there are more competitor restaurants to comparedelivery radii with. If there are not, the process exits in step 712.However, if there are more competitor restaurants, execution transitionsto step 706, where the next competitor restaurant is retrieved. In step708, the delivery radius of the subject restaurant and the presentcompetitor restaurant are compared and a delivery radius score iscomputed. In step 710, the delivery radius score for the presentcompetitor is saved for future use. Execution then returns to step 704.

II. Utility for Determining Competitive Advantage of a Subject Business

Turning to FIG. 15, a simplified system diagram depicting a system foranalyzing the strengths and weaknesses of a business. While the businessdiscussed herein is a restaurant, the system and method disclosed can beadapted to a wide variety of businesses by a person of skill in the art.

A restaurant server 16 is coupled to a database 14 that is maintained bya restaurant service. The database is coupled to the restaurantservice's system 13 for order placement by diners with memberrestaurants, and, maintains a record of any orders placed by diners inreal-time. The restaurant server 16 is coupled to various devices overthe Internet 18. The coupled devices can include, for example, an orderappliance 20, as described in earlier filed application [3211-0005], apersonal computer 22, a tablet computer 24 or a wireless mobile device26.

The restaurant server 16 operates to communicate data with any of thecoupled devices. For example, the restaurant server 16 can serve a webpage, or communicate with an application operating on any of the coupleddevices via, for example, XML over HTTP. Only restaurants that aremembers of the restaurant service are permitted to access thecompetitive advantage utility. Accordingly, an access means, such as ausername and password or some other unique identifier, are used to limitaccess to the competitive advantage web page or application. Each memberrestaurant is designated its own access means; i.e., a differentusername. In one embodiment of the disclosed competitive advantageutility, each order appliance may have a unique code assigned to it thatcorresponds to the restaurant it is assigned to. This unique code canmake for a simple access means, as the software operating on the orderappliance can automatically upload the unique code to the restaurantservice, which can assume that anyone with access to the order applianceis authorized to access the competitive utility. However, this does notpreclude the use of a password, or other identifier, coupled with theunique code assigned to the order appliance.

FIG. 16 depicts a flowchart describing a process by which a utility cangenerate a near real-time or real-time heat map of competitive activity.In step 952 a set of restaurants to be studied is assembled. The set ofassembled restaurants can include any arbitrary grouping of restaurants,such as, for example, i) the subject restaurant, ii) a group ofcompetitor restaurants that are manually chosen, iii) a group ofcompetitor restaurants that are determined automatically using any ofthe systems and methods discussed earlier in this disclosure, iv) agroup of all restaurants within a user defined distance of the subjectrestaurant. In step 954, a user of the utility selects a map areaincluding the subject restaurant within which to generate the heat map.This selection can be done via manual input; i.e., a text box, orthrough a graphical interface such as that described later herein. Theprocess of selecting the map area can be through the specification of adistance, so that a circular radius around the subject restaurant isincluded. Other ways that a map area can be specified include, but arenot limited to, 1) using the delivery area of the subject restaurant asstored in the restaurant service database; 2) selecting multiplecoordinates; i.e., latitude-longitude pairs, so that an elliptical areacan be specified, 3) selecting multiple coordinates to that arectangular or square area can be specified, and 4) selecting a seriesof coordinates tracing out an arbitrary closed area, with the lastcoordinate, being equal to the first coordinate, and thereby closing thearea. In step 956, a map displaying the members of the set ofrestaurants derived in step 952 and within the map area selected in step954 is displayed. In step 958, the utility monitors the database for acompetitive event, such as an order to a restaurant within the assembledstep, and in step 960, the event is rendered on the map.

One use of the process of FIG. 16 is to track competitive activity froma pool of competitor restaurants. For example, a restaurant manager mayconfigure the utility to replay the lunch period i.e., approximately11:30 AM to 1:30 PM in most U.S. cities, for several weeks, and the viewof FIGS. 18 and 19 will allow her to observe orders that are placed withcompetitors during those periods, and compare them to orders placed withthe manager's own restaurant. If a particular restaurant is generatinglarge number of orders in a period consistently, this could lead therestaurant manager to plan a promotion targeting that restaurant'sdiners, or to examine that restaurant's menu and the particular itemsbeing ordered with an eye toward improving the subject restaurant'sofferings. Another use of the process of FIG. 16 would be to track theordering activity of diners within the designated area, either as theorders occur in near real time or real time, or as a replay of prioractivity, such as the lunch period of some number of days. The areacould be set to, for example, the delivery area of the restaurant andorder flow could be examined. It could then be changed to be slightlygreater than the delivery area, and examined again. If order activity tocompetitors increases significantly, it would indicate to the manager ofthe subject restaurant that increasing the delivery area couldpotentially be profitable. In addition, the view of diner activity canbe used to target geographical locations, such as the distribution ofcoupons in a geographic area, or assembling a list of diner emails towhom to send promotions to. The following figures graphically illustratethese processes.

Turning to FIG. 17, a screen from the utility for determiningcompetitive advantage is depicted. This Screen depicts a portion of aneighborhood centered about a subject restaurant 1002. Using theinterface of this screen, a user can graphically set a radius in which aheat map will be generated, as required by the step 954 of the processof FIG. 16. In particular, as depicted, a first radius 1010 captures allrestaurants within a first specified distance, such as, for example, 1.5city blocks, while a second radius 1012 encompasses a larger distance,such as, for example, 4 blocks. The first radius encompasses competitiverestaurants 1004 a-1004 d, although it should be noted that 1004 d isbarely within the first radius 1010. The second radius encompasses notonly competitive restaurants 1004 a-1004 d, but also competitiverestaurants 1005 a-1005 j. However, neither radius encompassescompetitive restaurants 1006 a-1006 j.

Turning to FIG. 18 a, a screen depicting a map encompassing the firstradius 1010 of FIG. 17 is depicted. This view encompasses the subjectrestaurant 1002 and a small number of competitor restaurants 1004 a-1004d. While only a small number of competitors are depicted, there is ampleroom to display orders as they are logged, as depicted in FIG. 18 b.Similar to FIG. 18 b, FIG. 18 c displays orders in real time, but theorder balloons are tied to the locations of diners ordering meals. Onthe contrary, FIG. 19 a displays not only competitors 1004 a-1004 d, butalso competitors 1005 a-1005 j. While the greater number of competitorsgenerates more useful information, the display of that information isextremely crowded during “hot periods,” such as lunch and dinner time,when numerous orders are logged. This creates a “crowded” view of thenear real-time or real-time competitive Information, as depicted in FIG.19 b. Similarly, FIG. 19 c displays the same view as FIG. 19 b, butorder balloons are tied to the locations of diners ordering meals.

As discussed so far, the disclosed competitive advantage utility hasdisplayed all orders to restaurants within the assembled set. However,this is not a limitation of the disclosed utility; in particular, theorders that are examined can be more narrowly tailored, so that onlyorders of a specified item are displayed. For example, a restaurantmanager may be interested in evaluating her restaurant's burgerofferings versus competitors' burger offerings. Accordingly, the managercould monitor pickup and delivery orders over a specified area for aperiod of time to determine if a particular competitor is receiving adisproportionate share of burger orders.

The disclosed competitive advantage utility can also be used to generateheatmaps for aggregate competitive activity. Aggregate competitiveactivity refers to, for example, a collection of competitive activityaccumulated over a number of different competitors, a specific timeperiod, or collection of time periods, or a compilation of competitorsand time periods.

FIG. 20 depicts a flowchart of a process by which the competitiveadvantage utility can generate a heat map of aggregate competitiveactivity. In step 1052 a set of restaurants is assembled similar to step952. In step 1054, a user of the utility selects a map area includingthe subject restaurant within which to generate the heat map. In step1056, the user selects an area over which competitive activity will beaggregated, such as, for example, 4 square blocks, or 1 square mile.Similarly, in step 1058, the user selects a time period over which toaggregate competitive activity, such as 5 minutes, 30 minutes, or 1 day.

In step 1060, the user defines a playback schedule. In one embodiment ofthe disclosed competitive advantage utility, the playback period is asingle continuous time period; such as, for example, 11:30 AM-2:30 PM onApr. 17, 2012. However, in a separate embodiment of the disclosedcompetitive advantage utility, the playback schedule can be a number ofcontinuous time periods. For example, the playback schedule can be anumber of related continuous time periods, such as 4 consecutive Tuesdaylunch periods; i.e., 11:30 AM-2:30 PM on Apr. 17, 2012, 11:30 AM-2:30 PMon Apr. 24, 2012, 11:30 AM-2:30 PM on May 1, 2012 and 11:30 AM-2:30 PMon May 8, 2012. However, if the user of the competitive advantageutility desires to view unrelated time periods that is also within thescope of the disclosed competitive advantage utility. For example, if arestaurant manager desires to look at 3 random time periods, such as1:30-2:45 PM on Mar. 7, 2012, 11:15 AM-12:30 PM on Apr. 19, 2012, and7:30 PM-10:00 PM on May 4, 2012, the user could set that up as theplayback schedule.

In step 1062, the aggregate heat map is rendered. In particular,starting at the beginning of the first playback period, the competitiveadvantage utility displays an indication of the aggregate competitiveactivity within each activity aggregating area. As better illustrated inFIG. 21 a, the activity aggregating area effectively defines a gridwhich is overlaid on the area; i.e., the activity aggregating area iseffectively a quantum of area over which all orders are aggregatedwithin a time period as described below. The amount of competitiveactivity may be displayed as the raw competitive activity indicia; i.e.,the number of orders, in a particular aggregating time period. However,the amount of competitive activity is preferably displayed in a mannerthat shows the relative amount of activity in a particular area/timeperiod versus other area/time periods, as discussed later in thisdisclosure. The playback schedule is stepped through using theaggregating time period as a time quantum, and each activity aggregatingarea is updated for each time step. The amount of time that each step isdisplayed can be configured by the user. FIGS. 21 b-d illustrate threesteps of an aggregate heat map. It should be noted that certain activityindicia are displayed as larger than a single grid unit; however, withinthe present embodiment of the disclosed competitive advantage utility,each of these is assigned to a single activity aggregating area.

FIG. 22 illustrates a process by which the N×n sets of competitiveactivity that are displayed during the playback of an aggregated heatmapcan be ranked so that the relative amount of competitive activity can beproperly displayed. In step 1102, all orders that occur within theheatmap area and playback schedule are assembled into a first set oforders. In step 1104, the first set of orders is divided into N sets oforders, with each of the N set of orders corresponding to a specificactivity aggregating area; i.e., one of the grid sectors shown in FIG.21 a. In step 1106, the orders within each of the N sets is subdividedinto n additional sets of orders. Each of the n sets of orderscorresponds to a particular aggregating time period within the playbackschedule of a particular activity aggregating area, which is hereinafterreferred to as an activity set. For example, if the aggregating timeperiod is 5 minutes, and the playback schedule stretches from 11:30 AMto 12:30 PM on Apr. 17, 2012, activity sets for each of the N activityaggregating areas will be generated for 11:30:01 AM to 11:35 AM,11:35:01 AM to 11:40 AM, etc., with a total of 12 activity sets beinggenerated for each of the N activity aggregating areas.

In step 1108, the largest (H_(Nn)) and smallest (L_(Nn)) activity setwithin the playback schedule is determined. In step 1110 each of the N×nactivity sets are scored. One way of scoring the activity sets isthrough use of the following formula:

$S_{{Nn} = \frac{X_{Nn} - L_{Nn}}{H_{Nn} - L_{Nn}}}$

Where S is the score of the activity set corresponding to activity setindicia X; X is an indicia of the amount of competitive activity for aparticular quantum of aggregated competitive activity, H is the highestindicia of competitive activity for all activity sets of aggregatedcompetitive activity within the N×n sets of orders created by theprocess of FIG. 22, and L is the lowest indicia of competitive activityfor all activity sets of aggregated competitive activity within the N×nsets of orders created by the process of FIG. 22.

The above process can be made more useful by discarding all activitysets that have no competitive activity within them, which will allow formore meaningful comparisons of competitive activity. An additionaloptimization would be to discard all aggregating areas with nocompetitive activity prior to separating out the aggregating timeperiods within each aggregating area.

By using a ranking method, such as that set forth in FIG. 22, thecompetitive activity of each activity set can be displayed so that itsrelative amount of competitive activity is immediately apparent. Forexample, higher amounts of activity can be marked with darker shades ofred, while smaller amounts of competitive activity can be marked withlighter shades of red, orange, yellow, or gray, where little or noactivity is present. Alternatively, the size of a marking, such as acircle, can be changed based on the amount of the activity, or the fillof the marking can be changed, so that a low level of activity isdenoted by an almost empty marking while a high level of activity isdenoted by a nearly full marking. Examples of some of these are shown inFIG. 23.

As discussed above, different indicia may be used to define competitiveactivity. For example, the number of orders within an activity set canbe used as an effective indicia of competitive activity. Alternatively,the dollar amount of orders may be used. Other indicia are alsopossible, such as an additive or multiplicative combination of ordernumber and dollar amount, an indicia involving item count, or any otherindicia that is proportional to business activity for a restaurant.

As discussed above when discussing the generation of non-aggregatedheatmaps, aggregated heatmaps of specific items can also be generated.

In addition to displaying activity as a heatmap, the disclosedcompetitive advantage utility also can display competitive activity andsubject restaurant activity as a more traditional graph. For example,FIG. 24 illustrates a line graph that displays a restaurant's orders fortwo consecutive weekends. The lunch period for both days is aggregatedinto a single point labeled 12 PM and the dinner period for both days isaggregated into a single point labeled 8 PM. Similar graphs can be madefor particular items of the subject restaurant, all activity of a groupof competitor restaurants, all activity of all competitor restaurants,or specific items of a group of or all competitor restaurants.

Obviously, many additional modifications and variations of the presentinvention are possible in light of the above teachings. Thus, it is tobe understood that, within the scope of the appended claims, theinvention may be practiced otherwise than is specifically describedabove.

The foregoing description of the invention has been presented forpurposes of illustration and description, and is not intended to beexhaustive or to limit the invention to the precise form disclosed. Thedescription was selected to best explain the principles of the inventionand practical application of these principles to enable others skilledin the art to best utilize the invention in various embodiments andvarious modifications as are suited to the particular use contemplated.It is intended that the scope of the invention not be limited by thespecification, but be defined by the claims set forth below.

What is claimed is:
 1. A system for displaying a map of competitiveactivity pertinent to a subject restaurant, the system comprising: i) adatabase adapted to maintain an order queue; ii) a restaurant serverhaving access to the database; iii) a computer system coupled to theserver over a network; iv) wherein the computer system is adapted totransmit an identifier to the restaurant server, the identifieridentifying the subject restaurant to the restaurant service; v) whereinthe computer system is adapted to transmit an indicia indicative ofdesired competitor characteristics to the restaurant server; vi) whereinthe restaurant server is adapted to receive the identifier and theindicia indicative of desired competitor characteristics, and whereinthe restaurant server is adapted to query the database for competitorsof the subject restaurant based on the indicia of desired competitorcharacteristics and the identifier; vii) wherein the restaurant serveris adapted to compile a list of competitors of the subject restaurantand transmit the list of competitors to the computer system; viii)wherein the computer system receives the list of competitors, thecomputer system specifying an indicia indicative of a map area includingthe subject restaurant and transmitting the indicia indicative of a maparea to the restaurant server; ix) wherein the restaurant serverreceives the specified indicia indicative of a map area, the restaurantserver being adapted to query the database for competitor activitywithin the specified map area, the restaurant server being adapted toreceive at least one competitive activity report from the database; x)wherein the restaurant server is adapted to generate at least onecompetitive activity event based on the at least one competitiveactivity report, and transmit the at least one competitive activityevent to the computer system; and xi) wherein the computer system isadapted to render the at least one competitive activity event on adisplay.
 2. The system of claim 1 wherein the at least one competitiveactivity event is an order occurrence.
 3. The system of claim 1 whereinthe at least one competitive activity event is an amount of an order. 4.The system of claim 1 wherein the at least one competitive activityevent is a number of items associated with an order.
 5. The system ofclaim 1 wherein the at least one competitive activity event is displayedfor a fixed time period before being erased from the display.
 6. Thesystem of claim 1 wherein the indicia indicative of desired competitorcharacteristics includes a maximum distance from the subject restaurant.7. The system of claim 1 wherein the indicia indicative of desiredcompetitor characteristics includes a set of coordinates specifying amap area including the location of the subject restaurant.
 8. The systemof claim 1 wherein the indicia indicative of a map area is a maximumdistance from the subject restaurant.
 9. The system of claim 1 whereinthe indicia indicative of a map area is a set of coordinates.
 10. Thesystem of claim 1 wherein the computer system is further adapted totransmit at least one distance defining an aggregating area, at leastone time period defining an aggregating time period, and a playbackschedule, and wherein the at least one competitive activity report is aplurality of activity reports and wherein the restaurant server isadapted to organize the plurality of competitive activity reports intosets of competitive activity based on the aggregating area and theaggregating time period, and wherein the restaurant server is furtheradapted to generate a plurality of aggregated competitive activityevents based on the sets of competitive activity, and wherein therestaurant server is adapted to transmit the plurality of aggregatedcompetitive activity events to the computer system in accordance withthe playback schedule.
 11. The system of claim 10 wherein the playbackschedule is a single continuous time period.
 12. The system of claim 10wherein the playback schedule is a series of continuous time periods.13. The system of claim 10 wherein each of the plurality of aggregatedcompetitive activity reports is assigned a relative ranking.
 14. Thesystem of claim 13 wherein the relative ranking is assigned using theformula $S_{{Nn} = \frac{X_{Nn} - L_{Nn}}{H_{Nn} - L_{Nn}}}.$
 15. Amethod for displaying a map of competitive activity pertinent to asubject restaurant, the method operating on a system comprised of arestaurant server having access to a database and a computer systemnetworked with the restaurant server, the method comprising the stepsof: i) maintaining an order queue in the database; ii) transmitting anidentifier from the computer system to the restaurant server, theidentifier identifying the subject restaurant; iii) transmitting anindicia indicative of desired competitor characteristics from thecomputer system to the restaurant server; iv) the restaurant serverquerying the database for competitors of the subject restaurant based onthe indicia of desired competitor characteristics and the identifier; v)the restaurant server compiling a list of competitors of the subjectrestaurant; vi) the restaurant server transmitting the list ofcompetitors to the computer system; vii) the computer system receivingthe list of competitors; viii) the computer system specifying an indiciaindicative of a map area including the subject restaurant; ix) thecomputer system transmitting the indicia indicative of a map area to therestaurant server; x) the restaurant server receiving the indiciaindicative of a map area; xi) the restaurant server querying thedatabase for competitor activity within the specified map area; xii) therestaurant server receiving at least one competitive activity reportfrom the database; xiii) the restaurant server generating a competitiveactivity event from the at least one competitive activity report; xiv)the restaurant server transmitting the competitive activity event to thecomputer system; and xv) the computer system rendering the competitiveevent on a display.
 16. The method of claim 15 wherein the competitiveevent is an order occurrence.
 17. The method of claim 15 wherein thecompetitive event is an amount of an order.
 18. The method of claim 15wherein the competitive event is a number of items associated with anorder.
 19. The method of claim 15 wherein the competitive event isdisplayed for a fixed time period before being erased from the display.20. The method of claim 15 wherein the indicia indicative of desiredcompetitor characteristics includes a maximum distance from the subjectrestaurant.
 21. The method of claim 15 wherein the indicia indicative ofdesired competitor characteristics includes a set of coordinatesspecifying a map area including the location of the subject restaurant.22. The method of claim 15 wherein the indicia indicative of a map areais a maximum distance from the subject restaurant.
 23. The method ofclaim 15 wherein the indicia indicative of a map area is a set ofcoordinates.
 24. The method of claim 15 further comprising the steps of:xvi) the computer system transmitting to the restaurant server adistance defining an aggregating area, a time period defining anaggregating time period, and a playback schedule; xvii) the restaurantserver receiving the distance defining an aggregating area, the timeperiod defining an aggregating time period, and the playback schedule;xviii) wherein the at least one competitive activity report is aplurality of activity reports; xix) the restaurant server organizing theplurality of competitive activity reports into sets of competitiveactivity based on the aggregating area and the aggregating time period;xx) the restaurant server generating a plurality of aggregatedcompetitive activity events from the sets of competitive activity; xxi)the restaurant server transmitting the plurality of aggregatedcompetitive activity events to the computer system in accordance withthe playback schedule.
 25. The method of claim 15 wherein the playbackschedule is a single continuous time period.
 26. The method of claim 15wherein the playback schedule is a plurality of continuous time periods.27. The method of claim 15 wherein each of the plurality of aggregatedcompetitive activity events is assigned a relative ranking.
 28. Themethod of claim 15 wherein the relative ranking is assigned using theformula $S_{{Nn} = \frac{X_{Nn} - L_{Nn}}{H_{Nn} - L_{Nn}}}.$